Non-line-of-sight (NLOS) propagation conditions can severely degrade wireless localization accuracy due to the biases in range measurements. Machine learning methods such as support vector machine (SVM) can mitigate the effect of NLOS biases when sufficient labeled ranging measurements are available. This letter proposes a semi-supervised learning approach for NLOS identification and mitigation, which leverages low-cost unlabeled measurements by self-training to complement only a small portion of labeled ones. Experimental results show that the proposed semi-supervised approach can increase the NLOS identification probability from 90% to 94% and reduce the ranging error by 10% by exploiting the unlabeled measurements.

中文翻译：

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UWB测距误差缓解的半监督学习方法

由于视距测量的偏差，非视距（NLOS）传播条件会严重降低无线定位精度。当有足够的标记测距测量可用时，诸如支持向量机（SVM）之类的机器学习方法可以减轻NLOS偏差的影响。这封信提出了一种用于NLOS识别和缓解的半监督学习方法，该方法通过进行自我训练来利用低成本的未标记测量值，从而仅对一小部分被标记的测量值进行补充。实验结果表明，所提出的半监督方法可以利用未标记的测量值将NLOS识别率从90％提高到94％，并将测距误差降低10％。